Database for Prevalence and Determinants of Frailty in the Elderly with Quantifying Functional Mobility

Frailty is a common condition in older adults, characterized, among other things, by impairments in gait and movement patterns. The proposed FRAILPOL repository addresses the critical gap in geriatric research by offering a comprehensive, open-access, five body-worn inertial sensors (ankles, wrists, and back of sacrum) signals recorded during the Time Up and Go test of 668 participants, community-dwelling older adults. The gait data, as well as the stride-based spatio-temporal parameters along with demographic and health-related information, including cognitive health data, have been grouped according to established clinical criteria into three classes (robust, pre-frailty, and frailty). The technical verification includes classification by reporting results for both binary (robust, frailty) and multi-class (robust, pre-frailty, frailty) classification using classical machine learning models with acceptable accuracy.

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Publication Details

Journal
Scientific Data
Published
2026-02-18
DOI
https://doi.org/10.1038/s41597-026-06854-8
Primary Topic
Frailty in Older Adults
Type
article
Field-Weighted Citation Impact
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article

Database for Prevalence and Determinants of Frailty in the Elderly with Quantifying Functional Mobility

Arslan Amjad, Monika Błaszczyszyn, Jerzy Sacha, Agnieszka Szczęsna et al.
Scientific Data
Frailty in Older Adults
article

Database for Prevalence and Determinants of Frailty in the Elderly with Quantifying Functional Mobility

Arslan Amjad, Monika Błaszczyszyn, Jerzy Sacha, Agnieszka Szczęsna, Piotr Feusette, W. Wolański, Piotr Wittek, Zbigniew Borysiuk, Magdalena Sacha, Mariusz Konieczny, Robert Zieliński
article en

Abstract

Frailty is a common condition in older adults, characterized, among other things, by impairments in gait and movement patterns. The proposed FRAILPOL repository addresses the critical gap in geriatric research by offering a comprehensive, open-access, five body-worn inertial sensors (ankles, wrists, and back of sacrum) signals recorded during the Time Up and Go test of 668 participants, community-dwelling older adults. The gait data, as well as the stride-based spatio-temporal parameters along with demographic and health-related information, including cognitive health data, have been grouped according to established clinical criteria into three classes (robust, pre-frailty, and frailty). The technical verification includes classification by reporting results for both binary (robust, frailty) and multi-class (robust, pre-frailty, frailty) classification using classical machine learning models with acceptable accuracy.

Scientific DataVol. 13(1)
Silesian University of Technology (PL), Opole University of Technology (PL), University of Opole (PL)
Openalex Percentile: Top 49%
Frailty in Older Adults
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